Computer network architecture with machine learning and artificial intelligence and automated insight generation

ABSTRACT

Embodiments in the present disclosure relate generally to computer network architectures for machine learning, artificial intelligence, and automated insight generation. Embodiments of computer network architecture automatically identify, measure, and generate insight reports of underperformance and over performance in healthcare practices. Embodiments may generate the insight reports of performance either occasionally on demand, or periodically, or as triggered by events such as an update of available data. Embodiments may include a combination of system databases with data provided by system users, and third-party databases to generate the insight reports, including social media data, financial data, socio-economic data, medical data, search engine data, e-commerce site data, and other databases.

TECHNICAL FIELD

The present disclosure is related generally to computer networkarchitectures for machine learning and artificial intelligence thatenable a computer to learn by its own experience with users and by thepassage of time, thus enabling computer hardware to perform better,produce superior results for the user. Various embodiments apply to thehealthcare field and constitute a healthcare robot with artificialintelligence.

BACKGROUND

There is a longstanding frustrated need to have programmable hardwarefunction in a better manner, and particularly to have hardware besmarter and learn from its own experience, with machine learning andartificial intelligence.

The longstanding frustrated need is particularly acute in the healthcarefield. For example, in healthcare there is a need for computerarchitecture to automatically identify areas of underperformance andover performance in a healthcare practice for decision makers athealthcare providers and payers. Prior art computer systems requiremanual work by providers or payers to identify such areas ofperformance, and lack the artificial intelligence and machine learningnecessary for the computer systems to automatically general theseinsights about performance. Systems that generate such insightautomatically without human intervention would, of course, operate moreefficiently, reliably, and faster than systems requiring constant humaninput.

SUMMARY

The present disclosure is related generally to computer networkarchitectures for machine learning and artificial intelligence thatenable a computer to learn by its own experience with users and by thepassage of time, thus enabling superior performance by the computerhardware. Various embodiments apply to the healthcare field andconstitute a healthcare robot with artificial intelligence. Variousembodiments perform certain steps automatically without humanintervention, and hence are more efficient, reliable and faster thansystems without these automatic aspects.

The present disclosure describes a computer network architecture withmachine learning and artificial intelligence that automaticallyidentifies areas of underperformance and over performance in ahealthcare practice for decision makers at healthcare providers andpayers.

Decision makers at healthcare providers or payers may, for example,receive from the system an automatically generated and transmittedreport with identification of weaknesses to address in a healthcarepractice (for example, a patient subgroup suffering from a specificmedical condition that is receiving below benchmark outcomes or abovebenchmark costs and hospital stays) or strengths to exploit (forexample, operating or attending physicians for a specific medicalprocedure that are achieving above benchmark patient outcomes or belowbenchmark costs and hospital stays). The decision maker can then respondto these automatic insights to pursue higher quality healthcare.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 schematically illustrates an overview of a computer networkarchitecture including a machine learning system according to variousembodiments described herein constituting healthcare robot hardware.

FIG. 2 schematically illustrates various aspects of a computer networkarchitecture including a machine learning system according to variousembodiments described herein.

FIG. 3 is a prediction process of a computer network architectureincluding a machine learning prediction system according to variousembodiments described herein.

FIG. 4 schematically illustrates various aspects of a computer networkarchitecture including a machine learning prediction system according tovarious embodiments described herein.

FIG. 5 is a training process of a machine learning prediction systemaccording to various embodiments described herein.

FIG. 6 is a training and prediction process of a computer networkarchitecture including a machine learning prediction system according tovarious embodiments described herein.

FIG. 7 schematically illustrates components of a system according tovarious embodiments described herein.

FIG. 8 schematically illustrates an overview of a computer networkarchitecture including a machine learning prediction system and a webapplication configured to automatically generate data for patient riskscoring, according to various embodiments described herein.

FIG. 9 schematically illustrates various aspects of a computer networkarchitecture including a machine learning prediction system and a webapplication configured to automatically generate data for patient riskscoring, according to various embodiments described herein.

FIG. 10 Is a process for a patient risk scoring web applicationaccording to various embodiments described herein.

FIG. 11 schematically illustrates an overview of a computer networkarchitecture including a machine learning prediction system and a webapplication configured to automatically generate data for a patient riskscoring and a web application configured to automatically generateinsights, according to various embodiments described herein.

FIG. 12 schematically illustrates various aspects of a computer networkarchitecture including a machine learning prediction system and a webapplication configured to automatically generate data for a patient riskscoring and a web application configured to automatically generateinsights, according to various embodiments described herein.

FIG. 13 is a process for a user experience with an automated insightgeneration web application (AIGWA) according to various embodimentsdescribed herein.

DESCRIPTION OF AUTOMATED INSIGHT GENERATION WEB APPLICATION—LAYER 3

The Measurement and Meaning of Insights

Decision makers at healthcare providers or payers may, for example,receive from the system an automatically generated and transmittedreport with identification of weaknesses to address in a healthcarepractice (for example, a patient subgroup suffering from a specificmedical condition that is receiving below benchmark outcomes or abovebenchmark costs and hospital stays) or strengths to exploit (forexample, operating or attending physicians for a specific medicalprocedure that are achieving above benchmark patient outcomes or belowbenchmark costs and hospital stays). The decision maker can then respondto these automatic insights to pursue higher quality healthcare.

The following is an illustrative example of the work flow of a userexperience with an embodiment of an Automated Insight Generation WebApplication (AIGWA) 1116, which is adapted to execute the followingsteps, as shown in FIG. 13 with reference to FIG. 12:

-   -   a. A user logs into 1321 an Automated Insight Generation Web        Application (AIGWA) embodiment 1116.    -   b. The user interacts with the AIGWA software, requesting 1322        automated insight reports on specified topics be generated and        transmitted. The user may request insights for specific        subgroups of data, for example, for subgroups of patients,        healthcare providers, medical procedures, or other data        subgroups. For example, the user may request insights into the        performance of a certain hospital for knee replacement        procedures, for men in the 50 to 60 age group.    -   c. The user logs out 1323 of the AIGWA.    -   d. The AIGWA selects 1324 a metric (i.e., a statistic) to        measure the performance insight requested. For example, the        AIGWA may select the metrics of success of the procedure after 3        months, length of hospital stay, and total cost of the        procedure.    -   e. The AIGWA selects 1325 benchmarking data for the selected        metrics, which may be either (1) available empirical data 114,        132, 152 averages from sources available to the AIGWA, or (2)        the forecasted outcomes for the selected metrics from the        system's patient risk scoring web application 806, 906.    -   f. The AIGWA selects 1326 an estimate of error for each        statistic.    -   g. The AIGWA searches 1327 the data available 114, 132, 152 to        the AIGWA in the system for combinations of attributes and        computes statistics summarizing each subgroup.    -   h. The AIGWA automatically generates 1328 a list of patient        attributes and outcomes for those subgroups where the selected        metrics differ from the benchmarking data by a statistically        significant amount. That is, AIGWA identifies and measures        underperformance and over performance, which is referred to        herein as the “insights.”    -   i. The AIGWA filters and ranks 1329 the insights by impact on        the outcomes and selected metrics.    -   j. The AIGWA automatically generates 1330 an insight report of        the underperformance and over performance, and their metrics,        and transmits 1329 the report to the user. The insight reports        may be generated and transmitted either occasionally on demand        as requested, or periodically, or as triggered by events such as        an update of available data.

An embodiment may include a computer network architecture withartificial intelligence and machine learning, comprising a predictionmodule with a prediction generator and one or more an updated databases4109, 114, 132, 152; a learning module with a training submodule, inelectronic communication with the prediction module and the updateddatabase; a patient risk scoring web application in electroniccommunication with the prediction module and a user device; an automatedinsight generation web application (AIGWA) in electronic communicationwith the prediction module, the patient risk scoring web application;and a user device; and wherein the AIGWA is configured to execute theprocess steps of the workflow described immediately above.

An embodiment may use forecast models evaluating the most up-to-dateinformation 4019, 114, 132, 152 for patients to generate updatedforecasts of outcomes (i.e., the risk or probability of the variousoutcomes), as the patient progresses through the patient episode afterdischarge from the original caregiver facility. This data may includeactual outcomes for completed patient episodes, with projected outcomesof currently evolving and incomplete episodes. Historical data mayinclude information such as patient characteristics, dollar amounts forcosts, post-acute care history including dates of admission anddischarge from care facilities, and other information.

A Unique Combination of Data

The AIGWA 1116 automatically collects data for patient episodes from acommon prior time period, from various data bases which may includethird-party data bases 4109 or existing data bases in the embodiment114, 132, 152. The AIGWA loads the data into data bases 114, 132, 150 inthe embodiment.

The third-party databases 4109 may include medical claims data,prescription refill data, publicly available social media data, creditagency data, marketing data, travel website data, e-commerce websitedata, search engine data, credit card data, credit score and credithistory data, lending data, mortgage data, socio-economic data,financial data, travel data, geolocation data, telecommunications usagedata, and other third-party data bases. This unique combination ofthird-party databases gives the system 100 an edge in the quality of thepredictive models 142 used and in the forecasts produced. Furtherembodiments may also use this unique combination of third-party data,further combined with data generated by users 108 of the system 100prediction applications 106, to further enhance the quality of thepredictive models 142 and the forecasts produced.

Three Layers

Embodiments of the present invention may be viewed as having anunderlying data science layer. In the machine learning prediction system104, this may be viewed as the first layer. This layer automaticallyadds new data streams (data capture) from all sources to the predictiondatabase 114. This large expanding database contains all the datacaptured for all patients. The system makes predictions (e.g., thepatient risk profile for a patient) based on the database. Thepredictions can be subtle with a large number of parameters and detaileddemographic disaggregation of the patient groups.

The patient risk scoring web application 806,906 may be thought of as asecond layer that operates on top of the first layer 104, makingrequests 190 to the first layer, and receiving responses 192 back.

An automated insight generation web application (AIGWA) 1116 may operateon top of the second layer, as a third layer application.

FIG. 11 schematically illustrates an overview of a computer networkarchitecture including a machine learning prediction system and a webapplication configured to automatically generate data for a patient riskscoring web application 806, and a web application configured toautomatically generate insights 1116, according to various embodimentsdescribed herein.

FIG. 12 schematically illustrates various aspects of a computer networkarchitecture including a machine learning prediction system and a webapplication configured to automatically generate data for a patient riskscoring web application 906, and a web application configured toautomatically generate insights 1116, according to various embodimentsdescribed herein.

FIG. 13 is a process for a user experience with an automated insightgeneration web application (AIGWA) according to various embodimentsdescribed herein.

In various embodiments, the prediction application 106 in FIG. 1 andFIG. 9 may be embodied by the automated insight generation webapplication AIGWA 1116 and the patient risk scoring web application806,906 in FIG. 11 and FIG. 12, components of which are furtherdescribed in the discussion herein of related FIG. 8 and FIG. 9.

DESCRIPTION OF PIPELINE OF HEALTHCARE OUTCOME MODELS—LAYER 1

The present disclosure describes a novel network architecture configuredto produce and automatically improve predictive models, particularly forpatient healthcare outcomes regarding for example, cost, quality andpatient satisfaction. According to various embodiments, the networkarchitecture allows automatic machine learning as new data sources areadded, and new data is collected, and the predictive algorithms arerecalibrated and reselected using the expanded, and hence more reliable,data. This architecture addresses, for example, new waste identificationproblems in healthcare without having to proportionally increase laborcost. The above may enable users of the architecture to quickly realizethe value of new data.

Utilization of the novel architecture described herein may enable moreefficient operation, hosting, maintenance, integration and use of newdata and data sources, and continuous automatic improvement ofpredictive models by recalibration and reselection of algorithms basedon the expanding data.

Machine learning may incorporate predictive model algorithms to executepredictive analytical operations. Learning may be supervised orunsupervised. In general, a predictive model analyzes historical data toidentify patterns in the data. The patterns identified may includerelationships between various events, characteristics, or otherattributes of the data being analyzed. Modeling of such patterns mayprovide a predictive model whereby predictions may be made. Developmentof predictive models may employ mathematical or statistical modelingtechniques such as curve fitting, smoothing, and regression analysis tofit or train the data. Such techniques may be used to model thedistribution and relationships of the variables, e.g., how one or moreevents, characteristics, or circumstances (which may be referred to as“independent variables” or “predictor variables”) relate to an event oroutcome (which may be referred to as a “dependent variable” or“response”).

A machine learning process may include developing a predictive model.For example, a dataset comprising observed data may be input into amodeling process for mapping of the variables within the data. Themapped data may be used to develop a predictive model. The machinelearning process may also include utilizing the predictive model to makepredictions regarding a specified outcome that is a dependent variablewith respect to the predictive model. The machine may then be providedan input of one or more observed predictor variables upon which theoutput or response is requested. By executing the machine learningalgorithm utilizing the input, the requested response may be generatedand outputted. Thus, based on the presence or occurrence of a knownpredictor variable, the machine learning algorithm may be used topredict a related future event or the probability of the future event.

The present invention applies generally to predictive algorithmsinformed by data. When the data relates to medical patients andtreatments, and the algorithms correlate patient data with treatmentsand healthcare outcomes, then the system produces a pipeline ofpredictive models for healthcare outcomes that constantly improve andevolve with the machine learning and artificial intelligence of thepresent invention. For clarity and economy of description, the termsmachine learning and artificial intelligence are often usedinterchangeably herein.

Machine Learning and Artificial Intelligence Architecture

FIG. 1 provides a schematic overview of a flexible computer networkarchitecture 100 according to various embodiments. The networkarchitecture 100 may include a machine learning prediction system 104.The machine learning prediction system 104 may be configured to generatemachine learning algorithms comprising predictive models, automaticallyselect and calibrate predictive models, build databases containingmultiple predictive models for multiple model types as well as metricsfor each model, add new databases to the system, build existingdatabases integrating new data, update system predictive modelsutilizing new data, receive prediction requests, and generate outputresponses comprising predictions based on input parameters via executionof the machine learning algorithms. As described in more detail below,the machine learning prediction system 104 may include one or more localor distributed hardware units comprising servers, communication devices,data storage devices, and processors configured to execute instructionsfor performing the operations of the machine learning prediction system104.

The network architecture 100 may also include one or more predictionapplications 106. In some embodiments, the prediction application 106includes one or more web applications. The prediction application 106 orweb applications thereof may include or access episode request templatesfor defining prediction requests. Prediction applications 106 may begeneric for any end user, semi-generic, or customized for a single enduser.

In one embodiment, the web applications (i.e., prediction applications),the prediction module, and the learning module may reside in the cloudand be administered by the invention's operator. Web applications may bestructured for specific users, classes of users, templates or relateddata and predictions. In the healthcare industry, web applications mayseek prediction reports for outcomes of specific patient episodes, andfocus on factors such as patient guidance, automated healthcareperformance benchmarks, estimates of missing clinical episode costsrelated to incomplete health insurance claims, patient risk scoring,forecasting episode and annual costs, facilities rating systems adjustedfor risk, and other points.

In a healthcare embodiment, when an end user provides a request for aprediction report, the end user may give specific patient data (e.g.,patient metrics and treatment) to populate a selected episode profile ortemplate in a selected web application. The user requests a predictionreport, e.g., a prognosis (i.e., predicted outcomes). Other webapplications may give other reports. e.g., possible treatments,probability of each possible result of each treatment choice, economics,and schedule of prognosis and treatment. The web application thenautomatically accesses the prediction module and transmits the episodedata with the request, and receives back a prediction report from theprediction module.

Prediction applications 106 may be configured to interface one or moreuser devices 108 with the machine learning prediction system 104. Theuser device 108 may access or run the prediction application 106, whichmay be stored locally, remotely, or reside in the cloud. For example,the user device 108 may remotely access the prediction application 106with a browser through a network connection, such as an Internet,extranet, or VPN connection. User devices 108 may include an electroniccommunication device such as a computer, laptop, PDA, tablet, wearablecomputing device, or smart phone. The user device 108 will typicallyfunction as an end point with respect to the network architecture 100and interface users with the network architecture 100. Users may includecustomer or client end users, which may be in a commercial environmentsuch as in a SaaS environment. In an example healthcare application, auser or user device 108 may be associated with a customer or customergrouping, which may be a specific hospital, doctor office, insurancecompany, or grouping of multiple hospitals, doctor offices, or insurancecompanies, for example.

The machine learning prediction system 104 may include a predictionmodule 110 and a learning module 130. The prediction module 110 mayinclude a prediction generator 112 configured to process predictionrequests and generate prediction responses with respect to specifiedoutcomes. For example, prediction requests, which may also be referredto as report requests, may include episode data comprising observed datarelated to a response variable. Using the episode data, the predictiongenerator 112 may execute machine learning algorithms comprisingprediction models stored in a prediction database 114.

The learning module 130 may be configured to execute computer learningtasks including generating, calibrating, updating and evaluating systemprediction models. The learning module 130 may include a trainingsubmodule 150 configured to execute training functions, which mayinclude executing training requests, to fit data comprising observedepisode data to one or more model types defined in the machine learningprediction system 104.

The machine learning prediction system 104 may include one or moredatabases, each of which may comprise one or more member databases, forstoring episode data, metrics, model types, current best predictivemodels, archived predictive models, and other data from which themachine learning prediction system 104 may access to perform systemoperations. For example, the training submodule 150 may include atraining database 152 that stores observed episode data for trainingsystem prediction models. The training database 152 may collect andaccumulate data over time for use by the training submodule 150. In ahealthcare application, for example, the training database 152 mayinclude data relating to individual patients, episodes, and outcomes. Insome embodiments, the training database 152 includes a customer'sinstitutional data, which the training submodule 150 may access. In oneembodiment, the customer's institutional data is restricted to us onlyfor the customer. The training database 152 may also third partydatabases that may be used by the training submodule 150 to generate,update, or recalibrate system predictive models. Thus, the trainingsubmodule 150 may access or integrate third party databases. In someembodiments, the training database 152 includes one or more databaserepositories for medical data and socioeconomic data such as financialcredit histories from credit rating companies. The training database 152may reside in a single or multiple locations, be local or distributed,and may include third party databases accessible by the learning module130 or training submodule 150. In one embodiment, the training database152 may be divided or physical or logically partitioned into episodespecific data libraries.

A library of system prediction models and associated metrics generatedby the training submodule 150 may be stored in a learning database 132.The library may be used for evaluation of the archived models. Forexample, the learning database 132 may include multiple generatedpredictive models types of each of a plurality of specific episodestypes. The learning module 130 may periodically or upon the occurrenceof a predefined event, such as addition of new data or generation of newpredictive models, compare metrics across the predictive models in thelibrary to determine if a predictive model for a specific episodeperforms better than the predictive model for the episode that is storedin the prediction database 114, which the prediction module 110currently uses for responding to prediction requests for the episode.If, based on the metrics comparison, the learning module 130 determinesthat another predictive model performs better than the one stored in theprediction database 114, the learning module 130 may replace thepredictive model with the model determined to be the current best. Thelearning database 132 may maintain a record of the metrics andassociated predicative models for future evaluations. In someembodiments, the learning module 130 may test or update the regressioncalibration of one or more algorithm to be used, and add episode data tothe database to be kept and expanded and used by this and all subsequentqueries.

In various embodiments, the network architecture 100 or machine learningprediction system 104 may also include an administration module 170. Theadministration module 170 may be configured to allow systemsadministrators to develop and control the machine learning predictionsystem 104, such as defining episode types. For example, systemadministrators may define patient episode types such as types of patientconditions to be handled, their metrics, and training databases 152used. The machine learning prediction system 104 may be employed formachine learning and predictive analysis for any episode type. Episodetypes may include, for example, “hip and knee replacements underMedicare rules” or “pneumonia under Aetna Insurance” rules. Theadministration module 170 may provide, incorporate, or update episodetype definitions, e.g., types of patients, metrics and parameters, andillness types. The administration module 170 may also allow systemadministrators to define which training database 152 or predictiondatabase 114 each episode type will use. In some embodiments, systemadministrators may utilize the administration module 170 to add orintegrate additional databases into the training databases 152, whichmay include third party databases. In the above or another embodiment,system administrators may utilize the administration module 170 todefine which data each patient episode profile type will use. Forexample, utilizing the administration module 170, system administratorsmay define which parameters or attributes, such as predictor variables,associated with observed episode data is to be used for generatingpredictive models or predictions for specific episode types. Anadministrative application (see, e.g., FIG. 2) may also be used inaddition to or instead of an administration module 170.

Further Network Architecture

FIG. 2 illustrates an embodiment of the computer network architecture100 and machine learning prediction module 104 performing a machinelearning operation including one or more of training, evaluation,storage, or management of prediction functions. In particular, thelearning module 130 may be configured to execute a machine learningprocess wherein episodes, such as clinical episodes, are built andcontinually updated in the learning and training databases 132, 152,which represent the confluence of program specific episode logic andactual care provided to large populations of patients.

As introduced above with respect to FIG. 1, the learning module 130 mayinclude or access a training database 152, which may include episodespecific data libraries 152 a, 152 b, 152 c, from which a trainingsubmodule 150 may draw upon to generate or train system predictivemodels. In some embodiments, the training database 152 includes thirdparty or customer specific databases. Operation of the trainingsubmodule 150 may utilize actual observed data in the training database152 to generate predictive models and associated metrics for inclusionin a predictive model library 142 of the learning database 132. It willbe appreciated that episode specific data libraries 152 a, 152 b, 152 cmay be distributed among multiple hardware and locations. For example,episode specific library 152 a may be distributed on or among one ormore hardware locations, which may include one or more third partydatabases or customer databases. Episode specific library 152 b maysimilarly be distributed on or among one or more hardware locations,which may or may not include one or more hardware locations includingepisode specific library 152 a.

The learning module 130 or training submodule 150 thereof may include amodel type library. In the illustrated embodiment, the training database152 includes a model type library 153. In various embodiments, modeltypes (sometimes referred to as algorithms) may include one or more ofmulti-level models, random forest regression models, logisticalregression models, gamma-distributed regression models, linearregression models, or combinations thereof. Other model types may beused. In one embodiment, the learning module 130 may be configured toexpand utilization of additional model types or redefine or updatecurrent model types.

In various embodiments, the learning mode 130 may be configured toautomatically, such as periodically or upon the occurrence of a systemevent, generate training requests. For example, system events mayinclude addition of accessible episode data corresponding to specificepisodes, such as addition of or updating of data in the trainingdatabase 152. The learning module 130 may generate training requeststhat include all, multiple, or portions of one, more, or all datasets inan episode specific training database 152 a, 152 b, 152 c. For example,the learning module 130 may include a scheduling unit 136 configured toautomatically initiate a training process to test or update calibrationof the predictive models. For example, training may be initiated witheach query, number or volume of queries, periodically, upon introductionof new data, or other time period or event.

In some embodiments, system administrators may submit requests to thelearning module 130 instead of or in addition to automatic requestgeneration. In the illustrated embodiment, an administer device 109interfaces with the scheduling unit 136 via an administrator application172 to initiate the training process. The administrator device 109 mayaccess or run the administrator application 172, which may be storedlocally, remotely, or reside in the cloud. In one embodiment, theadministrative application 172 comprises a web application remotelyaccessible by the administrator device via a browser through a networkconnection, such as an Internet, extranet, or VPN connection. Theadministrator device 109 may include an electronic communication devicesuch as a terminal, computer, laptop, PDA, tablet, or smart phone. Thetraining request initiated by the administrator device 109 via theadministrator application 172 may include a request template providing amodel definition and dataset.

The model definition may identify parameters and episode attributes touse for model fitting in the training process. The dataset may includeobserved episode data including attribute values of predictor variablesand response variables. In some embodiments, the dataset provided in therequest may identify all or a portion of episode data stored in thetraining database 152 for the episode type that corresponds to the modeldefinition, which may be in addition to or instead of transmission ofnew data in the request.

As introduced above, the dataset may include observed episode attributevalues from a plurality of episodes corresponding to the episode typeindicated in the model definition. For example, the model definition mayspecify post-op knee replacement prognosis (Y) and identify attributes,e.g., age, weight, education, income, hospital location, procedurevariation, or other attributes (X), to be used in the modeling. Thedataset may then include a plurality of data points comprising episodeattribute values y₀, x₀₁, . . . , x_(0n) corresponding to the modeldefinition. In various embodiments, multiple pairs of model definitionsand datasets may be submitted to the training submodule 150, e.g., alist of requests comprising pairs of model definitions and datasets.

In various embodiments, the model definition may be generic to multipletypes of models in the model type library 144. For example, the modeldefinition may be applicable to two, more, or all types of models orcategory of types of models corresponding to an episode type. The modeldefinition may directly or indirectly identify applicable model types.Alternatively or additionally, the learning module 130 may be configuredsuch that model definitions are assumed to be applicable to all systemmodel types unless indicated otherwise.

The training submodule 150 may include a model processing unit 154configured to process training requests and generate predictive modelsaccording to the requests. The model processing unit 154 may employ oneor more worker processes 156 a, 156 b, 156 c to generate a fitted model,per the parameters of the request. Worker processes 156 a, 156 b, 156 cmay include modeling software programs, software functions, processes,or subroutines, for example. The model processing unit 154, or workerprocesses 156 a, 156 b, 156 c thereof, may access the model type library153 to obtain executable instructions for generating one or morepredictive models for one or more of the model types using the datasetspecified in the request.

In various embodiments, the model processing unit 154 may farm eachmodel definition/dataset pair to a worker process 156 a, 156 b, 156 c togenerate fitted models. In one example of the training process, one ormore worker process 156 a, 156 b, 156 c may each be tasked withgenerating a fitted model, per the parameters of the request. Asintroduced above, the machine learning prediction system 104 may beconfigured to generically support multiple types of models. Accordingly,multiple fitted models may be generated corresponding to one or moremodel types. In some embodiments, each worker process 156 a, 156 b, 156c may run a script, such as a Python script, to generate respectivefitted models. In one embodiment, worker processes 156 a, 156 b, 156 cmay access or be provided data specified in the request comprising oneor more datasets or portions thereof from one or more specified episodespecific training databases 152 a, 152 b, 152 c. Alternately, workerprocesses 156 a, 156 b, 156 c may be provided new or additional dataidentified by or included in the dataset of the training request. Forexample, the new or additional data may be accesses or obtained from athird party database. In some instances, all or a portion of the new oradditional data may be retained and stored in the training database 152and thereby expand available episode data for future fine tuning of thesystem's 104 episode modeling algorithms.

As introduced above, the model processing unit 154 may test generatedpredictive models, the metrics of which may be stored in the learningdatabase 132. In the illustrated embodiment, the worker processes 156 a,156 b, 156 c test the fitted models generated from the episodedefinition/dataset pair in the request. The testing process may includeone or more metric evaluations or other testing or evaluation schemes.In one example, worker processes 156 a, 156 b, 156 c may perform k-foldcross-validation to estimate prediction ability of the generated fittedmodels. In a further example, k-fold cross-validation may includetesting of a model via 5-fold cross-validation. K-fold cross-validationmay include partitioning a dataset into k different partitions or folds.Fitting the model may therefore include training k−1 subsets as trainingdata consistent with a model type and then testing the trained modelwith the remaining subset as a validation or test data. This may berepeated until all k subsets have been used as the validation or testdata. Calculated cross-validation errors across the folds may beaveraged. K-fold cross-validation may also be used to select the fittedmodel that is likely to be the most predictive with out-of-sample data.

The results of the model fitting and testing may be calculated by themodel processing unit 154. For example, worker processes 156 a, 156 b,156 c may calculate evaluation metrics descriptive of the generatedmodels, which may be used to compare models. Evaluation metrics mayinclude, for example, one or more metrics selected from RMSE, mean bias,mean absolute error, area under the curve on a receiver operatingcharacteristic plot, or combinations thereof. Other evaluation metricsmay be used. For example, suitable evaluation metrics may be selectedthat correspond to the model task or design such as binaryclassification, multiclass, or multi-label (ROC, Cohen's Kappa,confusion matrix, MCC, accuracy classification score, F1 score, F-betascore, average Hamming loss, Jaccard similarly coefficient score). Theresults of the model fitting and testing may be captured in a learningdatabase 132. The status of each model fitting worker process 156 a, 156b, 156 c may also be logged centrally, along with the status of theoverall request process.

The learning module 130 may also include a model evaluation unit 134configured to evaluate generated models in the predictive model library142. For example, an RMSE, mean bias, mean absolute error, area underthe curve on a receiver operating characteristic plot, and a date-timeof model fit for each generated predictive model may be sent to thelearning database 132 and stored in the predictive model library 142.The model evaluation unit 134 may use this captured test data to screenthe predictive quality of the model versus captured test data ofprevious model versions applicable to a corresponding episode type,which may include comparisons across model versions within a model typeor within multiple model types. If the model passes this screening, themodel evaluation unit 134 may automatically incorporate the model intothe prediction database 114 for use moving forward for future customerenvironments as the current best, or best-of-class, model for thecorresponding episode type or possibly additional episode types.

The learning module 130 may be configured to periodically or uponoccurrence of a predefined event test prediction accuracy of models inthe predictive model library 142 against new, current, or existingepisode data. For example, episode data may be dated in the trainingdatabase to track currentness of the data to test ability of generatedpredictive algorithms to predict responses based on current episodedata. Periodic prediction analysis may address drift, concept drift, orunintended honing or targeting of the model algorithm over time. Currentor new episode data may also be obtained from a third party database ormay be submitted in a request or imported into or associated with thetraining database 152, which may be an event that initiates analysis orevaluation of one or more system models, which may include best models,models that are not classified as current best models, or combinationsthereof. A low calculated prediction accuracy may initiate retraining,updating, recalibration, or replacement of a current best model with abetter performing model from the predictive model library 142 or a newlygenerated model fit and tested as above from new or existing episodedata.

Accordingly, the machine learning prediction system 104 may beconfigured for machine learning and artificial intelligence duringactual use by constant automatic fine-tuning of the calibration of thealgorithm and expansion of the databases. The learning module 130 maytest or update the regression calibration of system algorithms in theprediction database 114 and predictive model library 142 and add episodedata to the training database 152 to be kept and expanded and used bysubsequent queries.

Machine Learning Architecture

FIG. 3 illustrates a training process 300 that may be utilized by themachine learning prediction system 104 (see, e.g., FIGS. 1 & 2)according to various embodiments. The training process 300 may includereceiving a user request that includes a list of model definitions and adataset 301, which may be similar to the request described with respectto FIG. 2. The dataset may then be fitted to one or more model types302. For example, the training submodule may fit the dataset to one ormore models types, that is, a plurality of predictive model algorithmsmay be calibrated by the available data set. The model types, orpredictive algorithms, may include multi-level models, random forestregression models, logistical regression models, gamma-distributedregression models, linear regression models, combinations thereof, orother types. The fitted models (i.e., calibrated algorithms) may then betested by a 5-fold cross-validation 303, although, in some embodiments,the fitted model may be tested using other testing techniques. Themodels and the test results may be stored in the predictive modellibrary 304. The test results may include, for example, one or more offive cross-validation evaluation metrics selected from RMSE (root meanssquare error), mean bias, mean absolute error, area under the curve on areceiver operating characteristic plot, or date-time of model fit. Themodels in the predictive model library, which may include modelscurrently being used by the prediction module for servicing clientrequests, may be screened by comparing the evaluation metrics 305. Next,the screening results may be used to identify the best current model fora given response (that is, the algorithm with the best evaluationmetrics after calibration with the current data is the best currentalgorithm), and that model may be anointed best-of-class model based onthe performance metrics, that is, the preferred model for clients to usefor a given outcome 306. Thus, if the screen indicates that a predictivemodel in the prediction model library performs better than a modelcurrently being used by the prediction module for servicing the customerrequests, the newly anointed best-of-class model may replace that modelas the model used by the prediction module for servicing the customerrequest for the corresponding given response outcome.

The data used to calibrate, evaluate and select the algorithms (i.e.,the models) is constantly updated by addition of applicable databasesfrom users, and from third-party vendors, such as hospitals, medicalpractices, insurance companies, credit scoring agencies, and credithistory agencies. Also, the data available is updated by the data fromeach user request for an individual prediction for a patient episode.The accumulating data continually expands over time and shall become anincreasingly valuable asset. The training module may be automaticallyrerun periodically, occasionally, or with each individual user requestfor an individual prediction. That is, the algorithms may beautomatically recalibrated, re-evaluated and reselected periodically,occasionally, or otherwise, using the then current expanding database.More data is more statistically reliable, so automatically re-running ofthe training module with more data makes the model predictionsincreasingly better, that is this machine learning is improving theprediction results. This provides an automatic artificial intelligencepipeline of ever-improving predictive models.

Network Architecture with Web Applications

FIG. 4 illustrates operation of the prediction module 110 with respectto processing a prediction request according to various embodiments. Anend user may access the machine learning system 104 with a user device108 via a prediction application 106 (a web application) and request areport. In the request, the end user may provide specific episode data,e.g., specific patient data (patient metrics and treatment), to populatea selected episode profile or template in a selected web applicationassociated with the prediction application 106. The web application maybe specific to the request report, e.g., a response such as a prognosisor predicted outcome based on the provided episode data. The predictionapplication 106 may include other web applications that may provideother reports, e.g., possible treatments, probability of each possibleresult of each choice, economics, or schedule of prognosis andtreatment. The web application may then automatically access the machinelearning prediction system 104 and transmit the episode data with therequest, as indicated by arrow 190. The prediction module 110 mayinclude an application interface 116 configured to handle communicationbetween the prediction application 106 and the prediction module 110.

The request may be provide to a prediction generator 112 to generate therequested response. Using the desired response specified in the reportrequest and available episode data, the prediction generator 112 selectsthe appropriate model in the prediction database 114. An identifiedclient model may also be used. The prediction generator 112 may thenexecute the selected model utilizing the episode data specified in therequest to generate a prediction response for the specified outcome inthe request. The prediction module 110 may then provide the requestedreport including the prediction response, indicated by arrow 192, to theprediction application 106, via the application interface 116, forpresentation to the user with the user device 108.

Prediction Architecture

FIG. 5 illustrates a prediction process 500 that may be utilized by themachine learning prediction system 104 (see, e.g., FIGS. 1, 2, & 4)according to various embodiments. The process 500 includes receiving aprediction request 501. As described above with respect to FIG. 4, theprediction request may include a request for a report and includespecific episode data and identify a client model. Using the episodedata and best in class model in the prediction database corresponding tothe client model, the episode data may be input into the model and modelmay be run 502. The running of the model with the episode data generatesa prediction response based on the model output 503. The predictionresponse may then be provided to the client requester 504, e.g., theprediction module may transmit the response to the predictionapplication for display, storage, or routing to the user device oranother location specified by the client user or request.

Further Machine Learning Architecture

FIG. 6 illustrates a training and prediction process 600 that may beutilized by the machine learning prediction system 104 (see, e.g., FIGS.1, 2, & 4), according to various embodiments. The process 600 mayinclude receiving a request including a list of model definitions and adataset 601. The request may be automatically generated, submitted by asystem administrator similar to FIG. 2, or submitted by a customer user.The model definition may include identifying data, parameters, orcriteria for a modeling protocol. For example, the request may specify atraining dataset, outcomes to model, and factors that should be used topower the model. The dataset may then be fit to one or more model types,which may include multiple models corresponding to a single model type,based on the model definitions 602. The fitted model may then be tested,which may include estimating in and out of sample error 603. Asdescribed with respect to FIG. 2, testing may include cross-validationsuch as k-fold cross-validation (e.g., 5-fold cross-validation).Following testing, the model may be serialize or otherwise summarized ina way that allows for re-creation at prediction time 604. The model andtest results may be stored in the predictive model library 605. Theprocess may further include screening the predictive model library andanointing best-of-class models based on performance metrics as thepreferred model for clients to use for a given prediction responserequest 606.

The process may further include receiving service requests forpredictions relating to a specified clinical outcome 607. Next, cased onthe client request, a selection of the appropriate model from which togenerate the requested prediction response may be made 608. With theappropriate model selected, the next step may include refresh/generaterequested predictions for the clinical outcomes 609.

Components of an Architecture

Referring to FIG. 7, at least a portion of the present invention mayincorporate one or more computer apparatuses 700. Such a computerapparatus 700 may comprise a machine such as, but not limited to, acomputer system, apparatus, or other arrangement of one or morecomputing devices within which a set of instructions 724, when executed,may cause the machine to perform any one or more of the methodologies orfunctions discussed above alone or in conjunction other one or moreadditional machines associated with the network or system, e.g., networkarchitecture 100 or machine learning prediction system 104 describedherein, or another network or system. While a single machine 701 isillustrated in FIG. 7, the term “machine” shall also be taken to includeany collection of machines that individually or jointly execute a set(or multiple sets) of instructions 724 to perform any one or more of themethodologies discussed herein. The machine may be configured tofacilitate various operations conducted by the system. For example, themachine may be configured to, but is not limited to, assist the networkor system by providing processing power to assist with processing loadsexperienced in the network or system, by providing storage capacity forstoring instructions 724 or data traversing the network system, or byassisting with any other operations conducted by or within the networkor system.

In some embodiments, the computer apparatus 700 or a machine thereof mayoperate as a standalone device. In some embodiments, the computerapparatus 700 or a machine 701 thereof may be connected via acommunication network 735 to and assist with operations performed byother apparatuses, machines, or systems. For example, the computerapparatus 700 or a machine 701 thereof may be connected with anycomponent in the network or system. In a networked deployment, thecomputer apparatus 700 or a machine thereof may operate in the capacityof a server, server stack, or a client, such as a client user machine,in a server-client user network environment, or as a peer machine in apeer-to-peer (or distributed) network environment. The computerapparatus 701 or a machine thereof may comprise a server computer, aclient user computer, a personal computer (PC), a tablet PC, a laptopcomputer, a desktop computer, a control system, a network router, switchor bridge, or any machine capable of executing a set of instructions 724(sequential or otherwise) that specify actions to be taken by thatcomputer apparatus 700 or a machine 701 thereof. In one example, thecomputer apparatus 700 comprising one or more machines 701 is arrangedto have a distributed architecture employing a suitable model such as aclient-server model. In one embodiment, the computer apparatus 700 mayinclude a web service, e.g., service oriented architecture (SOA) orsimple object access protocol (SOAP). The computer apparatus 700 mayinclude a software as a service (SaaS) platform. The computer apparatus700 may include representational state transfer style resources orresource oriented architecture (ROA). The computer apparatus 700 mayinclude one or more processors, servers, databases, networks or networkdevices, and peripherals configured to obtain and transmit data andinitiate operations configured to perform in whole or in part theoperations of the system or platform thereof.

The computer apparatus 700 may include a processor 702, e.g., a centralprocessing unit (CPU), a graphics processing unit (GPU, or both), a mainmemory 704 and a static memory 706, which communicate with each othervia a bus 708. The computer apparatus 700 may further include a videodisplay unit 710, which may be, but is not limited to, a liquid crystaldisplay (LCD), a flat panel, a solid state display, or a cathode raytube (CRT). The computer apparatus 700 may include an input device 712,such as, but not limited to, a keyboard, a cursor control device 714,such as, but not limited to, a mouse, a disk drive unit 716, a signalgeneration device 718, such as, but not limited to, a speaker or remotecontrol, and a network interface device 720.

The disk drive unit 716 may include a machine-readable medium 722 onwhich is stored one or more sets of instructions 724, such as, but notlimited to, software embodying any one or more of the methodologies orfunctions described herein, including those methods illustrated above.For example, the learning or training submodule 130, 150 describedherein may include messaging or queuing processes or functions. Thus,the learning or training submodule may include Kue library to handletask distribution and coordination between worker process submodules,which may be backed by Redis in-memory key-value store with pub/sub. Theinstructions 724 may also reside, completely or at least partially,within the main memory 704, the static memory 706, or within theprocessor 702, or a combination thereof, during execution thereof by thecomputer system 700. The main memory 704 and the processor 702 also mayconstitute machine-readable media.

DESCRIPTION OF PATIENT RISK SCORING WEB APPLICATION—LAYER 2

The patient risk profile is a form of prediction. That is, the risk ofany specific patient outcome is a prediction of the likelihood of thatoutcome. The patient risk scoring profile may indicate a probability ofoccurring for each possible outcome of interest for that patient forthat episode type, such as durations, medical outcomes, financialparameters (e.g. costs), and other metrics.

Described here are computer network architectures for machine learning,and more specifically, to computer network architectures to forecast theprobabilities of the various possible outcomes of patient treatment, inthe context of program rules using combinations of defined patientclinical episode metrics and clinical financial metrics.

Program rules may be the rules of payor programs for treatment, such asMedicaid and Medicare rules, and of private payor programs by insurancecompanies or other payors, or programs that combine medical service withpayment of costs, such as the federal Veterans Administration orcombined private programs. The rules may be for specific treatmentepisode types (such as, e.g., a knee replacement or a coronary bypass),and may control factors such as the specific content of care, what drugsto use, duration of in-patient care, costs, cost caps, and bonuses orpenalties for the service providers pursuant to program rules forcertain performance parameters.

Patient clinic episode metrics may include, e.g., duration of inpatientcare, duration of outpatient care, success of treatment, morbidity ofthe patient, readmission of the patient, side effects of treatment, andothers.

Clinic financial metrics may include, e.g., total cost of care, cost ofmedical services, cost of drugs, bonuses and penalties charged to theservice provider for performance under program rules, and others.

Forecasts of the probabilities of various possible outcomes of patienttreatment are often referred to as patient risk scores or patient riskscoring.

The following is an example of the work flow of an embodiment of thepresent invention for a user experience with the embodiment:

-   -   1. Receive a user prediction request for patient risk scoring,        for a patient, for a specific clinical episode type.    -   2. Transmit the user prediction request for patient risk scoring        to the prediction module.    -   3. Automatically calibrate the correlation of demographic,        social and medical attributes of the patient, to the outcome of        specific patient clinical episode types.    -   4. Combine data of patient interactions with the healthcare team        and proprietary third-party patient attributes data into model        attributes.    -   5. Produce an interpretable schedule, adjusted for program        rules, of the marginal impact of patient attributes on the        outcomes of the patient episode.

Embodiments automatically use the pipeline of healthcare outcome modelsto select and apply the optimal predictive model for patient risk scoresspecific to certain patient clinical episode definitions.

In an embodiment, a computer network architecture with patient riskscoring, artificial intelligence and machine learning, comprises (1) aprediction module with a prediction generator and an updated database,(2) a learning module with a training submodule, in electroniccommunication with the prediction module and the updated database, (3) aweb application in electronic communication with both the predictionmodule, and a user device, and wherein, (4) the web application isadapted to:

-   -   a. Receive a user prediction request for patient risk scoring,        for a patient, for a specific clinical episode type,    -   b. Transmit the user prediction request for patient risk scoring        to the prediction module,    -   c. Automatically calibrate the correlation of demographic,        social and medical attributes of the patient, to the outcome of        specific patient clinical episode types to be analyzed,    -   d. Combine data of patient interactions with the healthcare team        and proprietary third-party patient attributes data into model        attributes.    -   e. Produce an interpretable schedule, adjusted for program        rules, of the marginal impact of patient attributes on the        outcomes of the patient episode.

In various embodiments, in FIG. 3, the request 301,501,601 may be arequest to select a preferred model to predict the patient risk profile306,606. In FIGS. 5 and 6, the prediction request 301,607 may be arequest to predict a patient risk profile. The requested predictiongenerated may be the requested patient risk profile generated 609.

In various embodiments, FIG. 3 and FIG. 6, the model definition and datasets 301,601 may be for patient risk scoring models and data. And theepisode data and client model 501 may be episode data and client modelsfor patient risk scoring.

In various embodiments, the prediction application 106 in FIG. 1 andFIG. 9 may be embodied by the patient risk scoring web application806,906 in FIG. 8 and FIG. 9.

The process 1000 for the patient risk scoring web application 806,906,according to various embodiments, is shown in FIG. 10. The patient riskscoring web application 806 receives 1001 a user prediction request forpatient risk scoring for a patient, for a specific clinical episodetype. The user creates this prediction request through a user device 108that accesses the web application 806.

The web application 806, 906 may then transmit 1002 the user predictionrequest 190 for patient risk scoring to the prediction module 110. Theprediction module may then access the learning module 130 toautomatically calibrate 1003 (or recalibrate) the correlation ofdemographic, social and medical attributes of the patient, to theoutcome of the specific patient clinical episode types to be analyzed,and select the optimal model for the prediction from the availablechoices. Alternatively, the prediction module may use a previouslycalibrated and selected optimal model for the request. If the modelshave already been calibrated, the step may recalibrate and update themodels with an expanded database that has been expanded since the lastupdate of the correlations.

The web application 906 may combine 1004 data of patient interactionswith the healthcare team and with the present invention, and proprietarythird-party patient attributes data and model attributes. All patientdata from all sources is kept in the various databases 114, 132, 152 ofthe present invention. Each prediction request 190 collects data for atleast one patient, which is added to the databases with each request. Asdescribed herein, the databases are also augmented with new data byadministrators 108, through an administrator application 172. Eachpatient interaction with the system 100 through some other webapplication 106, such as an automated insight generation web application806, 906 or other interaction with the healthcare team such as a doctorvisit, may also add data to the databases. Also, proprietary data fromthird party vendors about patient data for medical, financial, economic,and other data may be added to the databases. Individual data fromwhatever source is treated confidentially, and is not disclosed;however, the data can be aggregated across demographic groups, and alsoused to calculate derivative data, all of which can be used as data forthe variables in the functional equations of optimal prediction models.The patient risk scoring requested may then be disclosed to theindividual patient and his clinician, without disclosing anyconfidential data of any other individual.

A Focus of the Robot's Intelligence and Learning

This ongoing updating and recalibration of the optimal forecasting modelusing augmented data in 1003 and 1004 is an important source of machinelearning (i.e., artificial intelligence) in embodiments. One indicia ofartificial intelligence and machine learning is that when the systemreceives the same user request on two different days, the response onthe second day will be different and smarter because the database hasbeen expanded (i.e., the machine has learned), and the answer will bemore accurate because the model is recalibrated with more data (i.e.,the intelligence of the machine is increased). Because data is augmentedin part from the machine's own receipt of user requests (i.e., eachrequest with more patient data is added to the system databases), themachine is learning from its own experience and use. The more thecomputer architecture experiences, the more the computer architecturelearns, because it incorporates more data.

The web application 906 may then use the resulting data to produce 1005an interpretable schedule, adjusted by program rules, of the impact ofattributes in the patient risk profile on the outcomes of the patientclinical episode. The interpretable schedule is a schedule of theforecasted risk (i.e., probability) of each parameter of each possiblepatient episode outcome of interest for the patient's risk scoringrequest. The schedule is interpretable because it includes someaggregate statistics and information about the large database used tocalibrate and run the prediction model. (No confidential individual datais disclosed to the requesting user). For example, the size, averageage, average family income, and gender of the large database used todrive the projection might be indicated, to allow the clinician of thepatient in the user request to evaluate and interpret the resulting riskprofile based on his own judgment. Hence, the risk profile isinterpreted considering the patient population demographic groups usedto calibrate the model and forecast the risks, and distinguished bydemographic parameters of the patient of the request.

In various embodiments, the interpretable schedule may assess themarginal impact of patient attributes on the outcome of parameters ofthe patient clinical episode. For example, a correlation of patientattributes to clinical episode outcomes in a multiple regression tocalibrate and select a forecasting model, also may yieldcross-elasticities (i.e., sensitivities) of patient attributes toepisode outcomes. This cross-elasticity or sensitivity measures themarginal impact of patient attributes to episode outcomes.

Another unique improvement of various embodiments is that theinterpretable schedule reflects outcomes and probabilities (i.e., risks)after adjustment for medical program rules. For example, the total costof episode treatment is predicted for the total cost after adjustmentfor program rules for payment caps on total costs, and the resultingpayment of bonuses to, or the receipt of penalties from, serviceproviders.

Other Matters

Although various embodiments may be referred to as robots because oftheir machine learning and artificial intelligence, these embodimentsmight not have an anthropomorphic form or sound.

Dedicated hardware implementations including, but not limited to,application specific integrated circuits, programmable logic arrays andother hardware devices can likewise be constructed to implement themethods described herein. Applications that may include the apparatusand systems of various embodiments broadly include a variety ofelectronic and computer systems. Some embodiments implement functions intwo or more specific interconnected hardware modules or devices withrelated control and data signals communicated between and through themodules, or as portions of an application-specific integrated circuit.Thus, the example network or system is applicable to software, firmware,and hardware implementations.

In accordance with various embodiments of the present disclosure, theprocesses described herein are intended for operation as softwareprograms running on a computer processor. Furthermore, softwareimplementations can include, but are not limited to, distributedprocessing or component/object distributed processing, parallelprocessing, or virtual machine processing that may be constructed toimplement the methods described herein.

The present disclosure describes various modules, which may also bereferred to as sub-modules, generators, engines, systems, subsystems,components, units, and the like. Such modules may include functionallyrelated hardware, instructions, firmware, or software. Modules mayinclude physical or logical grouping of functionally relatedapplications, services, resources, assets, systems, programs, databases,or the like. Modules or hardware storing instructions or configured toexecute functionalities of the modules may be physically located in oneor more physical locations. For example, modules may be distributedacross one or more networks, systems, devices, or combination thereof.It will be appreciated that the various functionalities of thesefeatures may be modular, distributed, and/or integrated over one or morephysical devices. While the learning database as well as the trainingdatabases are illustrated individually as discrete elements, it will beappreciated that such logical partitions may not correspond to physicalpartitions of the data. For example, all or portions of the databasesmay reside or be distributed among one or more hardware locations.Further, while screening for best-of-class or current best model isgenerally described as including scanning the predictive model library142, this library 142 includes the models currently used by theprediction module 110, references to such models, evaluation metricsrelated to such data, or other data from which the screening may beperformed to properly screen the library 142 of predictive models forthe best-of-class or current best model.

The present disclosure contemplates a machine-readable medium 722containing instructions 724 so that a device connected to thecommunications network 735, another network, or a combination thereof,can send or receive voice, video or data, and to communicate over thecommunications network 735, another network, or a combination thereof,using the instructions. The instructions 724 may further be transmittedor received over the communications network 735, another network, or acombination thereof, via the network interface device 720.

While the machine-readable medium 722 is shown in an example embodimentto be a single medium, the term “machine-readable medium” should betaken to include a single medium or multiple media (e.g., a centralizedor distributed database, and/or associated caches and servers) thatstore the one or more sets of instructions. The term “machine-readablemedium” shall also be taken to include any medium that is capable ofstoring, encoding or carrying a set of instructions for execution by themachine and that causes the machine to perform any one or more of themethodologies of the present disclosure.

The terms “machine-readable medium,” “machine-readable device,” or“computer-readable device” shall accordingly be taken to include, butnot be limited to: memory devices, solid-state memories such as a memorycard or other package that houses one or more read-only (non-volatile)memories, random access memories, or other re-writable (volatile)memories; magneto-optical or optical medium such as a disk or tape; orother self-contained information archive or set of archives isconsidered a distribution medium equivalent to a tangible storagemedium. The “machine-readable medium,” “machine-readable device,” or“computer-readable device” may be non-transitory, and, in certainembodiments, may not include a wave or signal per se. Accordingly, thedisclosure is considered to include any one or more of amachine-readable medium or a distribution medium, as listed herein andincluding art-recognized equivalents and successor media, in which thesoftware implementations herein are stored.

The illustrations of arrangements described herein are intended toprovide a general understanding of the structure of various embodiments,and they are not intended to serve as a complete description of all theelements and features of the network architecture, systems, andprocesses that might make use of the structures described herein. Whilethe present disclosure generally describes the inventive networkarchitecture, systems, and process with respect to healthcareapplications, the healthcare field is but only one of many potentialapplications. Indeed, those having skill in the art will appreciate thatthe network architecture, systems, and processes described herein mayfind application in many industries. Other arrangements may be utilizedand derived therefrom, such that structural and logical substitutionsand changes may be made without departing from the scope of thisdisclosure. Figures are also merely representational and may not bedrawn to scale. Certain proportions thereof may be exaggerated, whileothers may be minimized. Accordingly, the specification and drawings areto be regarded in an illustrative rather than a restrictive sense.

Thus, although specific arrangements have been illustrated and describedherein, it should be appreciated that any arrangement calculated toachieve the same purpose may be substituted for the specific arrangementshown. This disclosure is intended to cover any and all adaptations orvariations of various embodiments and arrangements of the invention.Combinations of the above arrangements, and other arrangements notspecifically described herein, will be apparent to those of skill in theart upon reviewing the above description. Therefore, it is intended thatthe disclosure not be limited to the particular arrangement(s) disclosedas the best mode contemplated for carrying out this invention, but thatthe invention will include all embodiments and arrangements fallingwithin the scope of the appended claims.

The foregoing is provided for purposes of illustrating, explaining, anddescribing embodiments of this invention. Modifications and adaptationsto these embodiments will be apparent to those skilled in the art andmay be made without departing from the scope or spirit of thisinvention. Upon reviewing the aforementioned embodiments, it would beevident to an artisan with ordinary skill in the art that saidembodiments can be modified, reduced, or enhanced without departing fromthe scope and spirit of the claims described below.

What is claimed is:
 1. A computer network architecture with artificialintelligence and machine learning, comprising: a prediction module witha prediction generator and an updated database, a learning module with atraining submodule, in electronic communication with the predictionmodule and the updated database, and an automated insight generation webapplication (AIGWA) in electronic communication with both the predictionmodule, and a user device, wherein, the AIGWA is configured to executethe steps of: a. log a user into an Automated Insight Generation WebApplication (AIGWA) embodiment, b. receive a request from the user thatautomated insight reports be generated and transmitted, for specifiedtopics and requested subgroups of data, c. log out the user from theAIGWA, d. the AIGWA selects a metric to measure the performance insightrequested, e. the AIGWA selects benchmarking data for the selectedmetrics, which may be either (1) available empirical data averages fromsources available to the AIGWA, or (2) the forecasted outcomes for theselected metrics from the system's patient risk scoring web application,f. the AIGWA selects an estimate of error for each metric, g. the AIGWAsearches the data available to the AIGWA in the system for combinationsof metrics for subgroups of data, and computes statistics summarizingeach subgroup, h. the AIGWA automatically generates a list of thesubgroups where the selected metrics differ from the benchmarking databy a statistically significant amount of underperformance or overperformance, i. the AIGWA filters and ranks the insights by impact onthe outcomes and selected metrics, and j. the AIGWA automaticallygenerates an insight report of the underperformance and overperformance, and their metrics, and transmits the report to the user,occasionally as requested, or periodically, or when triggered by anevent, wherein, the learning module is configured to: receive a list ofalgorithm definitions and datasets for patient risk scoring,automatically calibrate one or more defined algorithms with thedatabase, test the calibrated algorithms with a plurality of evaluationmetrics, store the calibrated algorithms and evaluation metrics in alibrary, automatically select an algorithm for patient risk scoringbased on the evaluation metrics, update further the database with thirdparty data, and with user episode data, and re-execute the calibrate,test, store, and select steps after the update of the database step,wherein, the prediction generator is configured to: receive a userprediction request for patient risk scoring, including episode data anda client model, run the currently selected algorithm corresponding tothe user of the episode data, and generate patient risk scoringprediction output, generate a patient risk scoring prediction reportbased on the algorithm output, and transmit the patient risk scoringprediction report to the user, wherein, the algorithm definitions are oftypes that are members of the group comprising: multi-level models,random forest regression, logistical regression, gamma-distributedregression, and linear regression; the third-party data is from a partythat is a member of the group comprising: hospitals, medical practices,insurance companies, credit reporting agencies, and credit ratingagencies; the database includes patient medical data, patient personaldata, patient outcome data, and medical treatment data; the episode dataincludes individual patient medical data and personal data; and the useris a member of the group comprising: hospitals, medical practices, andinsurance companies, wherein the user device is remote from theprediction module, and the user device is a member of the groupcomprising: a computer, a desktop PC, a laptop PC, a smart phone, atablet computer, and a personal wearable computing device, wherein theweb application communicates with the user device by the Internet, or anextranet, or a VPN, or other network, and the web application is genericfor any user, or customized for a specific user, or class of user,wherein, the user prediction request requests calibration of thecorrelation of demographic, social and medical attributes of thepatient, to the outcome of a specific patient clinical episode type, andwherein the updated database includes data from at least one thirdparty, containing data of one or more types from the group consistingof: medical claims data, prescription refill data, publicly availablesocial media data, socio-economic data, credit agency data, marketingdata, travel website data, e-commerce website data, search engine data,credit card data, credit score and credit history data, lending data,mortgage data, financial data, travel data, geolocation data,telecommunications usage data, and other third-party databases.
 2. Acomputer network architecture with a web application for artificialintelligence and machine learning and automated insight generationcomprising: an automated insight generation web application (AIGWA) inelectronic communication with a prediction module, a patient riskscoring web application, and a user device, and wherein, the AIGWA isconfigured to: a. log a user into an Automated Insight Generation WebApplication (AIGWA) embodiment, b. receive a request from the user thatautomated insight reports be generated and transmitted, for specifiedtopics and requested subgroups of data, c. log the user out from theAIGWA, d. the AIGWA selects a metric to measure the performance insightrequested, e. the AIGWA selects benchmarking data for the selectedmetrics, which may be either (1) available empirical data averages fromsources available to the AIGWA, or (2) the forecasted outcomes for theselected metrics from the system's patient risk scoring web application,f. the AIGWA selects an estimate of error for each metric, g. the AIGWAsearches the data available to the AIGWA in the system for combinationsof metrics for subgroups of data, and computes statistics summarizingeach subgroup, h. the AIGWA automatically generates a list of thesubgroups where the selected metrics differ from the benchmarking databy a statistically significant amount of underperformance or overperformance, i. the AIGWA filters and ranks the insights by impact onthe outcomes and selected metrics, and j. the AIGWA automaticallygenerates an insight report of the underperformance and overperformance, and their metrics, and transmits the report to the user,occasionally as requested, or periodically, or when triggered by anevent, wherein, the learning module is configured to: receive a list ofalgorithm definitions and datasets for patient risk scoring,automatically calibrate one or more defined algorithms with thedatabase, test the calibrated algorithms with a plurality of evaluationmetrics, store the calibrated algorithms and evaluation metrics in alibrary, automatically select an algorithm for patient risk scoringbased on the evaluation metrics, update further the database with thirdparty data, and with user episode data, and re-execute the calibrate,test, store, and select steps after the update of the database step,wherein, the prediction generator is configured to: receive a userprediction request for patient risk scoring, including episode data anda client model, run the currently selected algorithm corresponding tothe user of the episode data, and generate patient risk scoringprediction output, generate a patient risk scoring prediction reportbased on the algorithm output, and transmit the patient risk scoringprediction report to the user, wherein, the algorithm definitions are oftypes that are members of the group comprising: multi-level models,random forest regression, logistical regression, gamma-distributedregression, and linear regression; the third-party data is from a partythat is a member of the group comprising: hospitals, medical practices,insurance companies, credit reporting agencies, and credit ratingagencies; the database includes patient medical data, patient personaldata, patient outcome data, and medical treatment data; the episode dataincludes individual patient medical data and personal data; and the useris a member of the group comprising: hospitals, medical practices, andinsurance companies, wherein, the user device is remote from theprediction module, and the user device is a member of the groupcomprising: a computer, a desktop PC, a laptop PC, a smart phone, atablet computer, and a personal wearable computing device, wherein theweb application communicates with the user device by the Internet, or anextranet, or a VPN, or other network, and the web application is genericfor any user, or customized for a specific user, or class of user, andwherein, the user prediction request requests calibration of thecorrelation of demographic, social and medical attributes of thepatient, to the outcome of a specific patient clinical episode type. 3.The computer architecture of claim 2, wherein the updated databaseincludes data from at least one third party, containing data of one ormore types from the group consisting of: medical claims data,prescription refill data, publicly available social media data,socio-economic data, credit agency data, marketing data, travel web sitedata, e-commerce web site data, search engine data, credit card data,credit score and credit history data, lending data, mortgage data,financial data, travel data, geolocation data, telecommunications usagedata, and other third-party databases.
 4. A computer networkarchitecture with an automated insight generation web application withartificial intelligence and machine learning, comprising: an automatedinsight generation web application (AIGWA) in electronic communicationwith a prediction module, a patient risk scoring web application and auser device, and wherein, the AIGWA web application is configured to: a.log a user into an Automated Insight Generation Web Application (AIGWA)embodiment, b. receive a request from the user that automated insightreports be generated and transmitted, for specified topics and requestedsubgroups of data, c. log the user out from the AIGWA, d. the AIGWAselects a metric to measure the performance insight requested, e. theAIGWA selects benchmarking data for the selected metrics, which may beeither (1) available empirical data averages from sources available tothe AIGWA, or (2) the forecasted outcomes for the selected metrics fromthe system's patient risk scoring web application, f. the AIGWA selectsan estimate of error for each metric, g. the AIGWA searches the dataavailable to the AIGWA in the system for combinations of metrics forsubgroups of data, and computes statistics summarizing each subgroup, h.the AIGWA automatically generates a list of the subgroups where theselected metrics differ from the benchmarking data by a statisticallysignificant amount of underperformance or over performance, i. the AIGWAfilters and ranks the insights by impact on the outcomes and selectedmetrics, and j. the AIGWA automatically generates an insight report ofthe underperformance and over performance, and their metrics, andtransmits the report to the user, occasionally as requested, orperiodically, or when triggered by an event.
 5. The computer networkarchitecture of claim 4: wherein, the learning module is configured to:receive a list of algorithm definitions and datasets for patient riskscoring, automatically calibrate one or more defined algorithms with thedatabase, test the calibrated algorithms with a plurality of evaluationmetrics, store the calibrated algorithms and evaluation metrics in alibrary, automatically select an algorithm for patient risk scoringbased on the evaluation metrics, update further the database with thirdparty data, and with user episode data, and re-execute the calibrate,test, store, and select steps after the update of the database step. 6.The computer network architecture of claim 4, wherein: the predictiongenerator is configured to: receive a user prediction request forpatient risk scoring, including episode data and a client model, run thecurrently selected algorithm corresponding to the user of the episodedata, and generate patient risk scoring prediction output, generate apatient risk scoring prediction report based on the algorithm output,and transmit the patient risk scoring prediction report to the user. 7.The computer network architecture of claim 4, wherein: the algorithmdefinitions are of types that are members of the group comprising:multi-level models, random forest regression, logistical regression,gamma-distributed regression, and linear regression; the third-partydata is from a party that is a member of the group comprising:hospitals, medical practices, insurance companies, credit reportingagencies, and credit rating agencies; the database includes patientmedical data, patient personal data, patient outcome data, and medicaltreatment data; the episode data includes individual patient medicaldata and personal data; and the user is a member of the groupcomprising: hospitals, medical practices, and insurance companies. 8.The computer network architecture of claim 4, wherein the user device isremote from the prediction module, and the user device is a member ofthe group comprising: a computer, a desktop PC, a laptop PC, a smartphone, a tablet computer, and a personal wearable computing device. 9.The computer network architecture of claim 4, wherein the webapplication communicates with the user device by the Internet, or anextranet, or a VPN, or other network, and the web application is genericfor any user, or customized for a specific user, or class of user, andwherein, the user prediction request requests calibration of thecorrelation of demographic, social and medical attributes of thepatient, to the outcome of a specific patient clinical episode type. 10.The computer architecture of claim 4, wherein the updated databaseincludes data from at least one third party, containing data of one ormore types from the group consisting of: medical claims data,prescription refill data, publicly available social media data,socio-economic data, credit agency data, marketing data, travel web sitedata, e-commerce web site data, search engine data, credit card data,credit score and credit history data, lending data, mortgage data,financial data, travel data, geolocation data, telecommunications usagedata, and other third-party databases.